Latest AI and machine learning research in emergency medicine for healthcare professionals.
The ability to predict drug overdose risk from a patient's medical records is crucial for timely intervention and prevention. Traditional machine learning models have shown promise in analyzing longitudinal medical records for this task. However, recent advancements in large language models (LLMs) offer an opportunity to enhance prediction performance by leveraging their ability to process long ...
Natural disasters increasingly threaten communities worldwide, creating an urgent need for rapid, reliable building damage assessment to guide emergency response and recovery efforts. Current methods typically classify damage in binary (damaged/undamaged) or ordinal severity terms, limiting their practical utility. In fact, the determination of damage typology is crucial for response and recover...
Overcrowding in emergency departments (ED) is a persistent problem exacerbated by population growth, emergence of pandemics, and increased morbidity...
The fusion of Large Language Models (LLMs) with recommender systems (RecSys) has dramatically advanced personalized recommendations and drawn extens...
Accurate rib fracture identification and classification are essential for treatment planning. However, existing datasets often lack fine-grained ann...
All-in-one image restoration, addressing diverse degradation types with a unified model, presents significant challenges in designing task-specific ...
The primary objective of phase I cancer clinical trials is to evaluate the safety of a new experimental treatment and to find the maximum tolerated ...
Unmanned aerial vehicles (UAVs), initially developed for military applications, are now used in various fields. As UAVs become more common across mu...
Blood cultures are often over ordered without clear justification, straining healthcare resources and contributing to inappropriate antibiotic use p...
Deep neural networks for medical image classification often fail to generalize consistently in clinical practice due to violations of the i.i.d. ass...
3D reassembly is a challenging spatial intelligence task with broad applications across scientific domains. While large-scale synthetic datasets hav...
BACKGROUND: Severe esophagogastric varices (EGVs) significantly affect prognosis of patients with hepatitis B because of the risk of life-threatening ...
The design of AI systems to assist human decision-making typically requires the availability of labels to train and evaluate supervised models. Freq...
The segmentation of pelvic fracture fragments in CT and X-ray images is crucial for trauma diagnosis, surgical planning, and intraoperative guidance...
In the United States alone accidental home deaths exceed 128,000 per year. Our work aims to enable home robots who respond to emergency scenarios in...
The development of artificial intelligence (AI) including generative large language models (LLMs) and software like ChatGPT is likely to significantly...
OBJECTIVE: Using artificial intelligence tools that work with different software architectures for both clinical and educational purposes in the medic...
Images of war are almost as old as war itself. From cave paintings to photographs of mobile devices on social media, humans always had the urge to c...
The opioid overdose epidemic remains a critical public health crisis, particularly in the United States, leading to significant mortality and societ...
BACKGROUND: Matching the necessary resources and facilities to attend to the needs of trauma patients is traditionally performed by clinicians using c...